Farmers’ perceptions about the influence of land fragmentation and land quality on sustainable land management in the upper lake Tana Basin: Evidence from Dera District

Figures & data

Table

Authors	Area	Effect on	Method	Indicator	Result	Conclusion	Recommendation
Blarel et al. (1992)	Ghana & Rwanda	Production	Pooled OLS	SI, N D, A	Yield(-) Cost(+)	LC is unlikely to Increase productivity Significantly	Focuses on reducing root causes of LF. Promote land & non land factor markets.
Parikh and Shah (1994)	Pakistan	Production Efficiency	SPFA (MLE)	N	Negative Relationship (no causality identified)	LF can be a result of technical inefficiency rather than a cause of it.	Increased education and availability of credit along with land consolidation would improve efficiency.
Jabarin and Epplin ()	Jordan	Production Cost; Efficiency	GLS	A	Cost(+) Production Efficiency(-)	LF is indeed an impediment to efficient wheat production.	Consolidate by encouraging land market.
Nguyen et al., (1996)	China	Production cost; Productivity	Production Function	A	Cost(+) Productivity(+)	Outcome could be expensive in terms of output forgone.	LC with less government intervention; improve land market, grain market and access to credit
Van Hung et al. (2007)	Vietnam	Labor Efficiency; Land Productivity	Standard FHHM Frontier Regression	N, Labor	Production(-) Labor use(+)	Real benefits to FHHs from LC may not be apparent until the real opportunity cost of farm labor begins to rise.	Consolidate by creating new off-farm jobs and movement of agricultural labor force to other sectors of the economy.
Tan et al. (2008)	China	Cost Efficiency	FHHM	A, D SI,	Cost(-)	The net impact on total production cost is not significant.	LC can stimulate technological adoption, but also can reduce agricultural employment and increase the rural labor surplus.
Rahiman and Rhman (2009)	Bangladesh	Production Efficiency	SPFA (MLE)	N	Productivity and efficiency(-)	Productivity and efficiency are adversely affected by fragmentation	Address the structural causes underlying the process of LF: law of inheritance and political economy of the agrarian sector.
Di Falco et al. (2010)	Bulgaria	Agro-biodiversity; Farm Profit	2SLS (Village fixed effect)	N, D, A	Profit(-) Agro biodiversity(+)	Policy measure of LC must carefully maintain the net effect of LF between Agro-biodiversity and profit.	Instead of LC improve functioning of land, labor, credit and food markets and access to improved technology and off farm employment.
Solomon Assefa (2013)	Ethiopia Tigray	Farm Productivity, efficiency	Stochastic frontier	A,N,I	Productivity and efficiency(-)	Productivity and efficiency are adversely affected by land fragmentation in Tigray region	Focus on land consolidation policy
AkaluTeshom et al. (2016)	Ethiopia Amhara	SLM and LF	Multivariate probit (MVP) model	A,N,D	SLM(-)	Land fragmentation influence farmers’ investments in SLM	Policy measures are needed to stop the further fragmentation of cultivated land.

SI=Simpson Index; N=Number of Plots; D=Average plot distance; A=Average Plot size; FHHM=Farm Household Model LF=Land Fragmentation; LC=Land Consolidation; HH=Household; TC=Transaction Cost; MVP=Multi variant Probit.

Source: Own Review (2019).

Figure 1. Map of study area.

Figure 2. Research design.

Figure 3. Sampling technique and procedures.

Table 1. Intensity of SLM investments at household level and plot level

SLM variables		Frequency	Percent
Bund HH level	User	154	79.4
	Non-User	40	20.6
Manure HH level	User	107	55.2
	Non-User	87	44.8
Permanent erosion control HH level	User	70	36.1
	Non-User	124	63.9
Manure plot level	With	250	23.6
	Without	809	76.4
Permanent erosion plot level	With	251	23.7
	Without	808	76.3
Chemical fertilizer plot level	With	928	87.6
	Without	131	12.4
Bund plot level	With	522	49.5
	Without	537	51.5

Table 2. Independent sample t-test on demographic and land fragmentation parameters

		Manure/compost investment (yes = 250) and (no = 809)		Bund investment (yes = 522) and (no = 537)		Permanent erosion control (yes = 251) and (no = 808)		Chemical fertilizer (yes = 928) and (no = 131)
Variables		t-ratio	Mean	t-ratio	Mean	t-ratio	Mean	t-ratio	Mean
Age of the HH head	User	−2.403(**)	47.27	−0.492	49.02	2.28(**)	46.02	3.6(***)	47.5
	Non-user		51.64		50.05		48.8		51.7
Labor intensity	User	2.656(***)	5.0198	2.89(***)	4.6	0.663	4.54	4.890(***)	4.6
	Non-user		4.5074		4.89		4.87		5.3
Area in Ha	User	1.575	0.29	−1.591	0.28	−2.48(**)	0.31	−.897	0.28
	Non-user		0.27		0.26		0.269		0.26
Distance in Km	User	−1.108(***)	0.35	−1.73(*)	1.18	−0.3	1.13	−5.5(***)	1.2
	Non-user		1.35		1.05		1.11		0.6
No of plot	User	1.453	6.26	1.987(**)	6.3	−1.494	6.38	1.400	6.4
	Non-user		6.48		6.56		6.61		6.68
Simpson	User	1.547	0.77	1.583	0.77	1.758(*)	0.73	3.04(***)	0.77
	Non-user		0.77		0.78		0.75		0.81

*** = Significant at 1%, ** = Significant at 5%, * = Significant at 10%.

Source: Own Survey (2019).

Table 3. Chi-square on land quality characteristics and ownership status

	Manure investment (yes = 250) and (no = 809)		Bund investment (yes = 522) and (no = 537)		Permanent erosion control (yes = 251) and (no = 808)		Chemical fertilizer (yes = 928) and (no = 131)
Variables	Chi-S	p-value	Chi-S	p-value	Chi-S	p-value	Chi-S	p-value
Slope status	31.34	0.000(***)	349.5	0.000(***)	47.41	0.000(***)	1.74	0.42
Soil depth	7.65	0.022(**)	57.62	0.000(***)	59.88	0.000(***)	14.3	0.001(***)
Soil fertility	5.38	0.068(*)	37.1	0.000(***)	34.30	0.000(***)	22.8	0.000(***)
Soil type	4.49	0.213	11.83	0.008(***)	2.23	0.525	7.22	0.065(*)
Ownership Status	20.98	0.001(***)	13.79	0.017(**)	32.95	0.000(***)	8.19	0.146

*, ** and *** = significance level at 10, 5 and 1%, respectively. Source: Own Survey (2019).

Table 4. Multivariate probit estimations for effects of land fragmentation on SLM

	Manure/compost investment (yes = 250) and (no = 809)		Bund investment (yes = 522) and (no = 537)		Permanent erosion control (yes = 251) and (no = 808)		Chemical fertilizer (yes = 928) and (no = 131)
Variables	Co-eff	SE	Co-eff	SE	Co-eff	SE	Co-eff	SE
Simpson I	1.25	0.7722	0.1715	0.6959	−.425	0.6954	−2.17(**)	0.9316
Number of P	−0.012898	0.0314	−0.032969	0.0282	0.0387	0.0273	−0.00281	0.0343
Distance	−0.6(***)	0.0366	−0.0465(*)	0.0281	0.0191	0.0289	0.184(***)	0.0308
Area	0.3957(*)	0.2160	0.14146	0.2094	0.47(**)	0.1953	.2856181	0.2851
Labor	.02039	0.0392	−0.099(***)	0.0345	−0.00556	0.0333	−0.1386(***)	0.0399
Age	−0.015(***)	0.0045	.0037108	0.0037	−.00527	0.0037	−0.0116(***)	0.0044
Ownership Status	−0.084276	0.0555	0.0669(*)	0.0397	−0.06646	0.0416	0.03963	0.0605
Slope Status	−0.281(***)	.07379	1.04(***)	0.0647	0.211(***)	0.058	0.1105852	.07234
Soil Depth	0.1291	0.091	−0.09189	0.0789	−0.24(***)	0.0743	0.064	0.091
Soil fertility	−0.1147	0.0969	0.0864	0.0852	−0.128689	0.0813	0.2838(***)	0.0956
Soil type	−0.0781479	0.0709	−0.072547	0.0585	0.121(**)	0.0588	−0.079754	0.0685
Constant	3.124(***)	0.6452	−1.169(**)	0.5731	−0.3283492	0.5735	2.14296(***)	0.7729
Predicted Pro(p)	0.2290024		0.4911,253		0.2391739		0.8763235
Joint probability (Success)		0.0244161
Joint probability (Failure)		0.0307773
No of observation		1059
Log likelihood		−1766.1967
Wald chi^2(44)		732.83
Probability of chi^2		0.000(***)

Table 5. Estimated correlation matrix

Estimated correlation matrix
	ρ1	ρ2	ρ3	ρ4
ρ1	1			(***)
ρ2	−0.0885623	1	(***)
ρ3	0.0346224	0.4237796(***)	1
ρ4	−0.2338349(***)	0.0083597	−0.0026726	1
Likelihood ratio test of: 21 = 31 = 41 = 32 = 42 = 43 = 0: chi^2(6) = 63.8101 Prob > chi^2 = 0.0000(***)

Note: Co-eff = Coefficient and SE = Standard errors.

*, ** and *** = significance level at 10, 5 and 1%, respectively. Source: Own Survey (2019).

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